Data classification method and device and electronic equipment

Abstract

The invention relates to the technical field of computers, in particular to a data classification method, a data classification device and electronic equipment. The method comprises the steps of obtaining at least two attribute values of a target attribute, and selecting one attribute value from the at least two attribute values as an initial clustering center; wherein the to-be-processed data comprises a plurality of samples; according to the distance between each attribute value and the initial clustering center, calculating a probability value that each attribute value can be used as the clustering center so as to determine the clustering center according to the probability value; clustering the attribute values of the target attributes based on the clustering centers, and dividing interval boundaries according to clustering results; and classifying the attribute values corresponding to the target attributes of the samples in the to-be-processed data according to an interval division result. According to the method, discrete processing can be carried out on continuous values, normal values and abnormal values are stored, and the normal values and the abnormal values can be classified into different categories.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a data classification method, a data classification device and electronic equipment. Background technique [0002] Binning can discretize continuous data and increase granularity. It can be used to denoise data, screen abnormal data, etc. Data binning methods can be divided into supervised methods and unsupervised methods. Among them, supervised methods can include chi-square binning, decision tree binning, and the like. Supervised methods include equal frequency binning, equidistant binning, cluster binning, etc. [0003] However, the existing binning methods all have certain defects. For example, supervised methods need to configure labels for samples during model training. For application scenarios such as anomaly detection, the sample data often has no labels, or it is impossible to exhaust the abnormal data in the sample data. However, the above-mentioned bin...

AI Technical Summary

Problems solved by technology

For application scenarios such as anomaly detection, the sample data often has no labels, or it is impossible to exhaust the abnormal data in the sample data.

However, the above-mentioned binning methods based on unsupervised mode are easy to confuse outliers and normal values, which makes the accuracy of abnormal data detection decrease.

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